Classes#

The material below builds on the section on binding custom types and reviews advanced scenarios involving object-oriented code.

Frequently used#

Click on the following nb::class_<..>::def_* members for examples on how to bind various different kinds of methods, fields, etc.

Type

method

Methods & constructors

.def()

Fields

.def_ro(), .def_rw()

Properties

.def_prop_ro(), .def_prop_rw()

Static methods

.def_static()

Static fields

.def_ro_static(), .def_rw_static()

Static properties

.def_prop_ro_static(), .def_prop_rw_static()

Subclasses#

Consider the following two data structures with an inheritance relationship:

struct Pet {
    std::string name;
};

struct Dog : Pet {
    std::string bark() const { return name + ": woof!"; }
};

To indicate the inheritance relationship to nanobind, specify the C++ base class as an extra template parameter of nb::class_<..>:

#include <nanobind/stl/string.h>

NB_MODULE(my_ext, m) {
    nb::class_<Pet>(m, "Pet")
       .def(nb::init<const std::string &>())
       .def_rw("name", &Pet::name);

    nb::class_<Dog, Pet /* <- C++ parent type */>(m, "Dog")
        .def(nb::init<const std::string &>())
        .def("bark", &Dog::bark);
}

Alternatively, you can also pass the type object as an ordinary parameter.

auto pet = nb::class_<Pet>(m, "Pet")
   .def(nb::init<const std::string &>())
   .def_rw("name", &Pet::name);

nb::class_<Dog>(m, "Dog", pet /* <- Parent type object */)
    .def(nb::init<const std::string &>())
    .def("bark", &Dog::bark);

Instances expose fields and methods of both types as expected:

>>> d = my_ext.Dog("Molly")
>>> d.name
'Molly'
>>> d.bark()
'Molly: woof!'

Automatic downcasting#

nanobind obeys type signature when returning regular non-polymorphic C++ objects: building on the previous example, consider the following function that returns a Dog object as a Pet base pointer.

m.def("pet_store", []() { return (Pet *) new Dog{"Molly"}; });

nanobind cannot safely determine that this is in fact an instance of the Dog subclass. Consequently, only fields and methods of the base type remain accessible:

>>> p = my_ext.pet_store()
>>> type(p)
<class 'my_ext.Pet'>
>>> p.bark()
AttributeError: 'Pet' object has no attribute 'bark'

In C++, a type is only considered polymorphic if it (or one of its base classes) has at least one virtual function. Let’s add a virtual default destructor to make Pet and its subtypes polymorphic.

struct Pet {
    virtual ~Pet() = default;
    std::string name;
};

With this change, nanobind is able to inspect the returned C++ instance’s virtual table and infer that it can be represented by a more specialized Python object of type my_ext.Dog.

>>> p = my_ext.pet_store()
>>> type(p)
<class 'my_ext.Dog'>
>>> p.bark()
'Molly: woof!'

Note

Automatic downcasting of polymorphic instances is only supported when the subtype has been registered using nb::class_<..>. Otherwise, the return type listed in the function signature takes precedence.

Overloaded methods#

Sometimes there are several overloaded C++ methods with the same name taking different kinds of input arguments:

struct Pet {
    Pet(const std::string &name, int age) : name(name), age(age) { }

    void set(int age_) { age = age_; }
    void set(const std::string &name_) { name = name_; }

    std::string name;
    int age;
};

Attempting to bind Pet::set will cause an error since the compiler does not know which method the user intended to select. We can disambiguate by casting them to function pointers. Binding multiple functions to the same Python name automatically creates a chain of function overloads that will be tried in sequence.

nb::class_<Pet>(m, "Pet")
   .def(nb::init<const std::string &, int>())
   .def("set", nb::overload_cast<int>(&Pet::set), "Set the pet's age")
   .def("set", nb::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");

Here, nb::overload_cast only requires the parameter types to be specified, and it deduces the return type.

Note

In cases where a function overloads by const-ness, an additional nb::const_ parameter is needed to select the right overload, e.g., nb::overload_cast<int>(&Pet::get, nb::const_).

To define overloaded constructors, simply declare one after the other using the normal .def(nb::init<...>()) syntax.

The overload signatures are also visible in the method’s docstring:

>>> help(my_ext.Pet)
class Pet(builtins.object)
 |  Methods defined here:
 |
 |  __init__(...)
 |      __init__(self, arg0: str, arg1: int, /) -> None
 |
 |  set(...)
 |      set(self, arg: int, /) -> None
 |      set(self, arg: str, /) -> None
 |
 |      Overloaded function.
 |
 |      1. ``set(self, arg: int, /) -> None``
 |
 |      Set the pet's age
 |
 |      2. ``set(self, arg: str, /) -> None``
 |
 |      Set the pet's name

The format of the docstring with a leading overload list followed by a repeated list with details is designed to be compatible with the Sphinx documentation generator.

Enumerations and internal types#

Let’s now suppose that the example class contains internal types like enumerations, e.g.:

struct Pet {
    enum Kind {
        Dog = 0,
        Cat
    };

    struct Attributes {
        float age = 0;
    };

    Pet(const std::string &name, Kind type) : name(name), type(type) { }

    std::string name;
    Kind type;
    Attributes attr;
};

The binding code for this example looks as follows:

nb::class_<Pet> pet(m, "Pet");

pet.def(nb::init<const std::string &, Pet::Kind>())
    .def_rw("name", &Pet::name)
    .def_rw("type", &Pet::type)
    .def_rw("attr", &Pet::attr);

nb::enum_<Pet::Kind>(pet, "Kind")
    .value("Dog", Pet::Kind::Dog)
    .value("Cat", Pet::Kind::Cat)
    .export_values();

nb::class_<Pet::Attributes>(pet, "Attributes")
    .def(nb::init<>())
    .def_rw("age", &Pet::Attributes::age);

To ensure that the nested types Kind and Attributes are created within the scope of Pet, the pet type object is passed as the scope argument of the subsequent nb::enum_<T> and nb::class_<T> binding declarations. The .export_values() function exports the enumeration entries into the parent scope, which should be skipped for newer C++11-style strongly typed enumerations.

>>> from my_ext import Pet
>>> p = Pet("Lucy", Pet.Cat)
>>> p.attr.age = 3
>>> p.type
my_ext.Kind.Cat
>>> p.type.__name__
'Cat'
>>> int(p.type)
1

Note

When the annotation nb::is_arithmetic() is passed to nb::enum_<T>, the resulting Python type will support arithmetic and bit-level operations like comparisons, and, or, xor, negation, etc.

nb::enum_<Pet::Kind>(pet, "Kind", nb::is_arithmetic())
   ...

By default, these are omitted.

Dynamic attributes#

Native Python classes can pick up new attributes dynamically:

>>> class Pet:
...     name = "Molly"
...
>>> p = Pet()
>>> p.name = "Charly"  # overwrite existing
>>> p.age = 2  # dynamically add a new attribute

By default, classes exported from C++ do not support this and the only writable attributes are the ones explicitly defined using class_::def_rw() or class_::def_prop_rw().

nb::class_<Pet>(m, "Pet")
    .def(nb::init<>())
    .def_rw("name", &Pet::name);

Trying to set any other attribute results in an error:

>>> p = my_ext.Pet()
>>> p.name = "Charly"  # OK, attribute defined in C++
>>> p.age = 2  # fail
AttributeError: 'Pet' object has no attribute 'age'

To enable dynamic attributes for C++ classes, the nb::dynamic_attr tag must be added to the nb::class_ constructor:

nb::class_<Pet>(m, "Pet", nb::dynamic_attr())
    .def(nb::init<>())
    .def_rw("name", &Pet::name);

Now everything works as expected:

>>> p = my_ext.Pet()
>>> p.name = "Charly"  # OK, overwrite value in C++
>>> p.age = 2  # OK, dynamically add a new attribute

Note that there is a small runtime cost for a class with dynamic attributes. Not only because of the addition of an instance dictionary, but also because of more expensive garbage collection tracking which must be activated to resolve possible circular references. Native Python classes incur this same cost by default, so this is not anything to worry about. By default, nanobind classes are more efficient than native Python classes. Enabling dynamic attributes just brings them on par.

Weak references#

By default, nanobind instances cannot be referenced via Python’s weakref class, and attempting to do so will raise an exception.

To support this, add the nb::is_weak_referenceable tag to the nb::class_ constructor. Note that this will increase the size of every instance by sizeof(void*) due to the need to store a weak reference list.

nb::class_<Pet>(m, "Pet", nb::is_weak_referenceable());

Extending C++ classes in Python#

Bound C++ types can be extended within Python, which is helpful to dynamically extend compiled code with further fields and other functionality. Bind classes with the is_final annotation to forbid subclassing.

Consider the following example bindings of a Dog and DogHouse class.

#include <nanobind/stl/string.h>

namespace nb = nanobind;

struct Dog {
    std::string name;
    std::string bark() const { return name + ": woof!"; }
};

struct DogHouse {
    Dog dog;
};

NB_MODULE(my_ext, m) {
    nb::class_<Dog>(m, "Dog")
       .def(nb::init<const std::string &>())
       .def("bark", &Dog::bark)
       .def_rw("name", &Dog::name);

    nb::class_<DogHouse>(m, "DogHouse")
       .def(nb::init<Dog>())
       .def_rw("dog", &DogHouse::dog);
}

The following Python snippet creates a new GuardDog type that extends Dog with an .alarm() method.

>>> import my_ext
>>> class GuardDog(my_ext.Dog):
...     def alarm(self, count = 3):
...         for i in range(count):
...             print(self.bark())
...
>>> gd = GuardDog("Max")
>>> gd.alarm()
Max: woof!
Max: woof!
Max: woof!

This Python subclass is best thought of as a “rich wrapper” around an existing C++ base object. By default, that wrapper will disappear when nanobind makes a copy or transfers ownership to C++.

>>> d = my_ext.DogHouse()
>>> d.dog = gd
>>> d.dog.alarm()
AttributeError: 'Dog' object has no attribute 'alarm'

To preserve it, adopt a shared ownership model using shared pointers or intrusive reference counting. For example, updating the code as follows fixes the problem:

#include <nanobind/stl/shared_ptr.h>

struct DogHouse {
    std::shared_ptr<Dog> dog;
};

>>> d = my_ext.DogHouse()
>>> d.dog = gd
>>> d.dog.alarm()
Max: woof!
Max: woof!
Max: woof!

Overriding virtual functions in Python#

Building on the previous example on inheriting C++ types in Python, let’s investigate how a C++ virtual function can be overridden in Python. In the code below, the virtual method bark() is called by a global alarm() function (now written in C++).

#include <iostream>

struct Dog {
    std::string name;
    Dog(const std::string &name) : name(name) { }
    virtual std::string bark() const { return name + ": woof!"; }
};

void alarm(Dog *dog, size_t count = 3) {
    for (size_t i = 0; i < count; ++i)
        std::cout << dog->bark() << std::endl;
}

Normally, the binding code would look as follows:

#include <nanobind/stl/string.h>

namespace nb = nanobind;
using namespace nb::literals;

NB_MODULE(my_ext, m) {
    nb::class_<Dog>(m, "Dog")
       .def(nb::init<const std::string &>())
       .def("bark", &Dog::bark)
       .def_rw("name", &Dog::name);

    m.def("alarm", &alarm, "dog"_a, "count"_a = 3);
}

However, this don’t work as expected. We can subclass and override without problems, but virtual function calls originating from C++ aren’t being propagated to Python:

>>> class ShihTzu(my_ext.Dog):
...     def bark(self):
...         return self.name + ": yip!"
...

>>> dog = ShihTzu("Mr. Fluffles")

>>> dog.bark()
Mr. Fluffles: yip!

>>> my_ext.alarm(dog)
Mr. Fluffles: woof!     # <-- oops, alarm() is calling the base implementation
Mr. Fluffles: woof!
Mr. Fluffles: woof!

To fix this behavior, you must implement a trampoline class. A trampoline has the sole purpose of capturing virtual function calls in C++ and forwarding them to Python.

#include <nanobind/trampoline.h>

struct PyDog : Dog {
    NB_TRAMPOLINE(Dog, 1);

    std::string bark() const override {
        NB_OVERRIDE(bark);
    }
};

This involves an additional include directive and the line NB_TRAMPOLINE(Dog, 1) to mark the class as a trampoline for the Dog base type. The count (1) denotes to the total number of virtual method slots that can be overridden within Python.

Note

The number of virtual method slots is used to preallocate memory. Trampoline declarations with an insufficient size may eventually trigger a Python RuntimeError exception with a descriptive label, e.g.:

nanobind::detail::get_trampoline('PyDog::bark()'): the trampoline ran out of
slots (you will need to increase the value provided to the NB_TRAMPOLINE() macro)

The macro NB_OVERRIDE(bark) intercepts the virtual function call, checks if a Python override exists, and forwards the call in that case. If no override was found, it falls back to the base class implementation. You will need to replicate this pattern for every method that should support overriding in Python.

The macro accepts an variable argument list to pass additional parameters. For example, suppose that the virtual function bark() had an additional int volume parameter—in that case, the syntax would need to be adapted as follows:

std::string bark(int volume) const override {
    NB_OVERRIDE(bark, volume);
}

The macro NB_OVERRIDE_PURE() should be used for pure virtual functions, and NB_OVERRIDE() should be used for functions which have a default implementation. There are also two alternate macros NB_OVERRIDE_PURE_NAME() and NB_OVERRIDE_NAME() which take a string as first argument to specify the name of function in Python. This is useful when the C++ and Python versions of the function have different names (e.g., operator+ vs __add__).

The binding code needs a tiny adaptation (highlighted) to inform nanobind of the trampoline that will be used whenever Python code extends the C++ class.

nb::class_<Dog, PyDog /* <-- trampoline */>(m, "Dog")

If the nb::class_<..> declaration also specifies a base class, you may specify it and the trampoline in either order. Also, note that binding declarations should be made against the actual class, not the trampoline:

nb::class_<Dog, PyDog>(m, "Dog")
   .def(nb::init<const std::string &>())
   .def("bark", &PyDog::bark); /* <--- THIS IS WRONG, use &Dog::bark */

With the trampoline in place, our example works as expected:

>>> my_ext.alarm(dog)
Mr. Fluffles: yip!
Mr. Fluffles: yip!
Mr. Fluffles: yip!

The following special case needs to be mentioned: you may not implement a Python trampoline for a method that returns a reference or pointer to a type requiring type casting. For example, attempting to expose a hypothetical virtual method const std::string &get_name() const as follows

const std::string &get_name() const override {
    NB_OVERRIDE(get_name);
}

will fail with a static assertion failure:

include/nanobind/nb_cast.h:352:13: error: static_assert failed due to requirement '...'
"nanobind::cast(): cannot return a reference to a temporary."

This is not a fluke. The Python would return a str object that nanobind can easily type-cast into a temporary std::string instance. However, when the virtual function call returns on the C++ side, that temporary will already have expired. There isn’t a good solution to this problem, and nanobind therefore simply refuses to do it. You will need to change your approach by either using bindings instead of type casters or changing your virtual method interfaces to return by value.

Operator overloading#

Suppose that we’re given the following Vector2 class with a vector addition and scalar multiplication operation, all implemented using overloaded operators in C++.

class Vector2 {
public:
    Vector2(float x, float y) : x(x), y(y) { }

    Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
    Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
    Vector2 operator-() const { return Vector2(-x, -y); }
    Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
    Vector2& operator*=(float v) { x *= v; y *= v; return *this; }

    friend Vector2 operator*(float f, const Vector2 &v) {
        return Vector2(f * v.x, f * v.y);
    }

    std::string to_string() const {
        return "[" + std::to_string(x) + ", " + std::to_string(y) + "]";
    }
private:
    float x, y;
};

The following snippet shows how the above operators can be conveniently exposed to Python.

#include <nanobind/operators.h>

NB_MODULE(my_ext, m) {
    nb::class_<Vector2>(m, "Vector2")
        .def(nb::init<float, float>())
        .def(nb::self + nb::self)
        .def(nb::self += nb::self)
        .def(nb::self *= float())
        .def(float() * nb::self)
        .def(nb::self * float())
        .def(-nb::self)
        .def("__repr__", &Vector2::to_string);
}

Note that a line involving nb::self like

.def(nb::self * float())

is really just short hand notation for

.def("__mul__", [](const Vector2 &a, float b) {
    return a * b;
}, nb::is_operator())

This can be useful for exposing additional operators that don’t exist on the C++ side, or to perform other types of customization. The nb::is_operator() flag marker is needed to inform nanobind that this is an operator, which returns NotImplemented when invoked with incompatible arguments rather than throwing a type error.

When binding in-place operators such as operator+=, and when their implementation is guaranteed to end with return *this, it is recommended that you set a return value policy of rv_policy::none, i.e.,

.def(nb::self += nb::self, nb::rv_policy::none)

Otherwise, the function binding will return a new copy of the object, which is usually not desired.

Binding protected member functions#

It’s normally not possible to expose protected member functions to Python:

class A {
protected:
    int foo() const { return 42; }
};

nb::class_<A>(m, "A")
    .def("foo", &A::foo); // error: 'foo' is a protected member of 'A'

On one hand, this is good because non-public members aren’t meant to be accessed from the outside. But we may want to make use of protected functions in derived Python classes.

The following pattern makes this possible:

class A {
protected:
    int foo() const { return 42; }
};

class Publicist : public A { // helper type for exposing protected functions
public:
    using A::foo; // inherited with different access modifier
};

nb::class_<A>(m, "A") // bind the primary class
    .def("foo", &Publicist::foo); // expose protected methods via the publicist

This works because &Publicist::foo is exactly the same function as &A::foo (same signature and address), just with a different access modifier. The only purpose of the Publicist helper class is to make the function name public.

If the intent is to expose protected virtual functions which can be overridden in Python, the publicist pattern can be combined with the previously described trampoline:

class A {
public:
    virtual ~A() = default;

protected:
    virtual int foo() const { return 42; }
};

class Trampoline : public A {
public:
    NB_TRAMPOLINE(A, 1);
    int foo() const override { NB_OVERRIDE(foo); }
};

class Publicist : public A {
public:
    using A::foo;
};

nb::class_<A, Trampoline>(m, "A") // <-- `Trampoline` here
    .def("foo", &Publicist::foo); // <-- `Publicist` here, not `Trampoline`!

Binding classes with template parameters#

nanobind can also wrap classes that have template parameters. Consider these classes:

struct Cat {};
struct Dog {};

template <typename PetType> struct PetHouse {
    PetHouse(PetType& pet);
    PetType& get();
};

C++ templates may only be instantiated at compile time, so nanobind can only wrap instantiated templated classes. You cannot wrap a non-instantiated template:

// BROKEN (this will not compile)
nb::class_<PetHouse>(m, "PetHouse");
    .def("get", &PetHouse::get);

You must explicitly specify each template/type combination that you want to wrap separately.

// ok
nb::class_<PetHouse<Cat>>(m, "CatHouse")
    .def("get", &PetHouse<Cat>::get);

// ok
nb::class_<PetHouse<Dog>>(m, "DogHouse")
    .def("get", &PetHouse<Dog>::get);

If your class methods have template parameters you can wrap those as well, but once again each instantiation must be explicitly specified:

typename <typename T> struct MyClass {
    template <typename V> T fn(V v);
};

nb::class_<MyClass<int>>(m, "MyClassT")
    .def("fn", &MyClass<int>::fn<std::string>);

Tag-based polymorphism#

The section on automatic downcasting explained how nanobind can infer the type of polymorphic C++ objects at runtime. It can be desirable to extend this automatic downcasting behavior to non-polymorphic classes, for example to support tag-based polymorphism. In this case, instances expose a method or field to identify their type.

For example, consider the following class hierarchy where Pet::kind serves this purpose:

#include <nanobind/nanobind.h>

namespace nb = nanobind;

enum class PetKind { Cat, Dog };

struct Pet { const PetKind kind; };
struct Dog : Pet { Dog() : Pet{PetKind::Dog} { } };
struct Cat : Pet { Cat() : Pet{PetKind::Cat} { } };

namespace nb = nanobind;

NB_MODULE(my_ext, m) {
    nb::class_<Pet>(m, "Pet");
    nb::class_<Dog>(m, "Dog");
    nb::class_<Cat>(m, "Cat");

    nb::enum_<PetKind>(m, "PetKind")
        .value("Cat", PetKind::Cat)
        .value("Dog", PetKind::Dog);

    m.def("make_pet", [](PetKind kind) -> Pet* {
        switch (kind) {
            case PetKind::Dog: return new Dog();
            case PetKind::Cat: return new Cat();
        }
    });
}

This code initially doesn’t work as expected (the make_pet function binding always creates instances of the Pet base class).

>>> my_ext.make_pet(my_ext.PetKind.Cat)
<my_ext.Pet object at 0x10305ee10>

>>> my_ext.make_pet(my_ext.PetKind.Dog)
<my_ext.Pet object at 0x10328e530>

To fix this, partially specialize the type_hook class to provide the type_hook<T>::get() method:

namespace nanobind::detail {
    template <> struct type_hook<Pet> {
        static const std::type_info *get(Pet *p) {
            if (p) {
                switch (p->kind) {
                    case PetKind::Dog: return &typeid(Dog);
                    case PetKind::Cat: return &typeid(Cat);
                }
            }
            return &typeid(Pet);
        }
    };
} // namespace nanobind::detail

The method will be invoked whenever nanobind needs to convert a C++ pointer of type T* to a Python object. It should inspect the instance and return a pointer to a suitable RTTI record. With this override, downcasting works as expected:

>>> my_ext.make_pet(my_ext.PetKind.Cat)
<my_ext.Cat object at 0x104da6e10>

>>> my_ext.make_pet(my_ext.PetKind.Dog)
<my_ext.Dog object at 0x104da6ef0>

Binding unions#

nb::class_<..> can also be used to provide bindings for unions. A basic and useless example:

union Example {
    int ival;
    double dval;

    std::string to_string(size_t active_idx) const {
        return active_idx == 1 ? std::to_string(dval) : std::to_string(ival);
    }
};
static_assert(sizeof(Example) == sizeof(double));

nb::class_<Example>(m, "Example")
    .def_rw("ival", &Example::ival)
    .def_rw("dval", &Example::dval)
    .def("to_string", &Example::to_string);

>>> u = my_ext.Example()
>>> u.ival = 42
>>> u.to_string(0)
'42'
>>> u.dval = 1.25
>>> u.to_string(1)
'1.250000'

Direct binding of union variant members is only safe if all members of the union are trivially copyable types (as in this example), but more complex unions can also be supported by binding lambdas or member functions that enforce the necessary invariants.

This is a low-level feature and should be used with care; even when all members are trivially copyable, reading from a union member other than the most recently written one produces undefined behavior in C++. Unless you need to bind an existing API that uses union types, you’re probably better off using std::variant<..>, which knows what member is active and can thus enforce all the ncessary invariants for you.

Pickling#

To pickle and unpickle objects bound using nanobind, expose the __getstate__ and __setstate__ methods. They should return and retrieve the internal instance state using representations that themselves support pickling. The example below, e.g., does this using a tuple.

The __setstate__ method should construct the object in-place analogous to custom __init__-style constructors.

#include <nanobind/stl/tuple.h>

struct Pet {
     std::string name;
     int age;
     Pet(const std::string &name, int age) : name(name), age(age) { }
};

NB_MODULE(my_ext, m) {
    nb::class_<Pet>(m, "Pet")
       // ...
       .def("__getstate__", [](const Pet &pet) { return std::make_tuple(pet.name, pet.age); })
       .def("__setstate__", [](Pet &pet, const std::tuple<std::string, int> &state) {
             new (&pet) Pet(
                 std::get<0>(state),
                 std::get<1>(state)
             );
       });
 }

Customizing Python object creation#

Sometimes you might need to bind a class that can’t be constructed in the usual way:

class Pet {
  private:
    Pet(/* ... */);
  public:
    static std::unique_ptr<Pet> make(std::string name, int age);
    void speak();
};

You can use .def_static() to produce bindings that let you write Pet.make("Fido", 2) in Python, just like you would write Pet::make("Fido", 2) in C++. But sometimes it’s nice to provide a more Pythonic interface than that, like Pet("Fido", 2). To do that, nanobind lets you override __new__.

Since this is a rarely-used feature in Python, let’s recap. Object initialization in Python occurs in two phases:

  • the constructor, __new__, allocates memory for the object;

  • the initializer, __init__, sets up the object’s initial state.

So far, all the ways we’ve seen of binding C++ constructors (nb::init<..>(), .def("__init__", ...)) produce Python object initializers. nanobind augments these with its own Python object constructor, which allocates a Python object that has space in its memory layout for the C++ object to slot in. The __init__ method then fills in that space by calling a C++ constructor.

This split between __new__ and __init__ has a lot of benefits, including a reduction in unnecessary allocations, but it does mean that anything created from Python must be able to control where its C++ innards are stored. Sometimes, as with the example of Pet above, that’s not feasible. In such cases, you can go down one level and override __new__ directly:

nb::class_<Pet>(m, "Pet")
    .def(nb::new_(&Pet::make), "name"_a, "age"_a)
    .def("speak", &Pet::speak);

Passing nb::new_ to .def() here creates two magic methods on Pet:

  • A __new__ that uses the given function to produce a new Pet. It is converted to a Python object in the same way as the return value of any other function you might write bindings for. In particular, you can pass a nb::rv_policy as an additional argument to .def() to control how this conversion occurs.

  • A __init__ that takes the same arguments as __new__ but performs no operation. This is necessary because Python automatically calls __init__ on the object returned by __new__ in most cases.

You can provide a lambda as the argument of nb::new_. This is most useful when the lambda returns a pointer or smart pointer; if it’s returning a value, then .def("__init__", ...) will have better performance. Additionally, you can chain multiple calls to .def(nb::new_(...)) in order to create an overload set. The following example demonstrates both of these capabilities:

nb::class_<Pet>(m, "Pet")
    .def(nb::new_([]() { return Pet::make(getRandomName(), 0); }))
    .def(nb::new_(&Pet::make), "name"_a, "age"_a)
    .def("speak", &Pet::speak);

If you need even more control, perhaps because you need to access the type object that Python passes as the first argument of __new__ (which nb::new_ discards), you can write a .def_static("__new__", ...) and matching .def("__init__", ...) yourself.

Note

Unpickling an object of type Foo normally requires that Foo.__new__(Foo) produce something that __setstate__ can be called on. Any custom nb::new_ methods will not satisfy this requirement, because they return a fully-constructed object. In order to maintain pickle compatibility, nanobind by default will add an additional __new__ overload that takes no extra arguments and calls the nanobind built-in inst_alloc(). This won’t make your class constructible with no arguments, because there’s no corresponding __init__; it just helps unpickling work. If your first nb::new_ method is one that takes no arguments, then nanobind won’t add its own, and you’ll have to deal with unpickling some other way.